Overview

Dataset statistics

Number of variables14
Number of observations8514
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory931.3 KiB
Average record size in memory112.0 B

Variable types

NUM13
BOOL1

Reproduction

Analysis started2020-08-06 05:39:39.047847
Analysis finished2020-08-06 05:40:18.402095
Duration39.35 seconds
Versionpandas-profiling v2.8.0
Command linepandas_profiling --config_file config.yaml [YOUR_FILE.csv]
Download configurationconfig.yaml

Warnings

date is highly correlated with Unnamed: 0 and 2 other fieldsHigh correlation
Unnamed: 0 is highly correlated with date and 2 other fieldsHigh correlation
retweets_count is highly correlated with likes_countHigh correlation
likes_count is highly correlated with retweets_countHigh correlation
Cases_x is highly correlated with Unnamed: 0 and 2 other fieldsHigh correlation
Deaths_x is highly correlated with Unnamed: 0 and 2 other fieldsHigh correlation
replies_count is highly skewed (γ1 = 33.08903532) Skewed
Unnamed: 0 has unique values Unique
likes_count has 4190 (49.2%) zeros Zeros
replies_count has 5334 (62.6%) zeros Zeros
retweets_count has 5662 (66.5%) zeros Zeros
stopwords_count has 90 (1.1%) zeros Zeros
Sentiment has 1990 (23.4%) zeros Zeros
Target has 107 (1.3%) zeros Zeros

Variables

Unnamed: 0
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct count8514
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4256.5
Minimum0
Maximum8513
Zeros1
Zeros (%)< 0.1%
Memory size66.5 KiB
2020-08-06T01:40:18.627915image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile425.65
Q12128.25
median4256.5
Q36384.75
95-th percentile8087.35
Maximum8513
Range8513
Interquartile range (IQR)4256.5

Descriptive statistics

Standard deviation2457.924429
Coefficient of variation (CV)0.5774519979
Kurtosis-1.2
Mean4256.5
Median Absolute Deviation (MAD)2128.5
Skewness0
Sum36239841
Variance6041392.5
2020-08-06T01:40:18.812484image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
20471< 0.1%
 
13461< 0.1%
 
54161< 0.1%
 
74651< 0.1%
 
13221< 0.1%
 
33711< 0.1%
 
54241< 0.1%
 
74731< 0.1%
 
13301< 0.1%
 
33791< 0.1%
 
Other values (8504)850499.9%
 
ValueCountFrequency (%) 
01< 0.1%
 
11< 0.1%
 
21< 0.1%
 
31< 0.1%
 
41< 0.1%
 
ValueCountFrequency (%) 
85131< 0.1%
 
85121< 0.1%
 
85111< 0.1%
 
85101< 0.1%
 
85091< 0.1%
 

date
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count117
Unique (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20200423.67371388
Minimum20200211
Maximum20200610
Zeros0
Zeros (%)0.0%
Memory size66.5 KiB
2020-08-06T01:40:18.982931image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum20200211
5-th percentile20200312
Q120200327
median20200416
Q320200508
95-th percentile20200602
Maximum20200610
Range399
Interquartile range (IQR)181

Descriptive statistics

Standard deviation88.72362317
Coefficient of variation (CV)4.392166452e-06
Kurtosis-0.8602382792
Mean20200423.67
Median Absolute Deviation (MAD)90
Skewness0.2982919838
Sum1.719864072e+11
Variance7871.881308
2020-08-06T01:40:19.166706image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
202003212042.4%
 
202004171441.7%
 
202003291431.7%
 
202003221361.6%
 
202003181351.6%
 
202004151341.6%
 
202004241311.5%
 
202003121281.5%
 
202003261281.5%
 
202003231281.5%
 
Other values (107)710383.4%
 
ValueCountFrequency (%) 
202002111< 0.1%
 
202002133< 0.1%
 
202002151< 0.1%
 
202002162< 0.1%
 
202002171< 0.1%
 
ValueCountFrequency (%) 
20200610370.4%
 
20200609490.6%
 
20200608490.6%
 
20200607410.5%
 
20200606330.4%
 

likes_count
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct count576
Unique (%)6.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean767.1612638007987
Minimum0
Maximum160784
Zeros4190
Zeros (%)49.2%
Memory size66.5 KiB
2020-08-06T01:40:19.357098image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q35
95-th percentile450
Maximum160784
Range160784
Interquartile range (IQR)5

Descriptive statistics

Standard deviation7949.310811
Coefficient of variation (CV)10.36198148
Kurtosis233.9179632
Mean767.1612638
Median Absolute Deviation (MAD)1
Skewness14.62936162
Sum6531611
Variance63191542.38
2020-08-06T01:40:19.543986image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0419049.2%
 
1121514.3%
 
25076.0%
 
32903.4%
 
41551.8%
 
51121.3%
 
6941.1%
 
7750.9%
 
8490.6%
 
9440.5%
 
Other values (566)178320.9%
 
ValueCountFrequency (%) 
0419049.2%
 
1121514.3%
 
25076.0%
 
32903.4%
 
41551.8%
 
ValueCountFrequency (%) 
1607843< 0.1%
 
1475973< 0.1%
 
1431004< 0.1%
 
1422041< 0.1%
 
1422031< 0.1%
 

replies_count
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct count230
Unique (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean88.18722104768617
Minimum0
Maximum64215
Zeros5334
Zeros (%)62.6%
Memory size66.5 KiB
2020-08-06T01:40:19.736751image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile56
Maximum64215
Range64215
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1526.553768
Coefficient of variation (CV)17.31037388
Kurtosis1265.134424
Mean88.18722105
Median Absolute Deviation (MAD)0
Skewness33.08903532
Sum750826
Variance2330366.407
2020-08-06T01:40:19.910609image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0533462.6%
 
1134415.8%
 
22753.2%
 
31351.6%
 
4560.7%
 
5450.5%
 
13400.5%
 
16340.4%
 
12320.4%
 
26310.4%
 
Other values (220)118814.0%
 
ValueCountFrequency (%) 
0533462.6%
 
1134415.8%
 
22753.2%
 
31351.6%
 
4560.7%
 
ValueCountFrequency (%) 
642153< 0.1%
 
436902< 0.1%
 
184172< 0.1%
 
157242< 0.1%
 
138793< 0.1%
 

retweets_count
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct count414
Unique (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean179.75933756166313
Minimum0
Maximum34926
Zeros5662
Zeros (%)66.5%
Memory size66.5 KiB
2020-08-06T01:40:20.090094image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile209
Maximum34926
Range34926
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1707.044549
Coefficient of variation (CV)9.49627748
Kurtosis229.6932844
Mean179.7593376
Median Absolute Deviation (MAD)0
Skewness14.40865622
Sum1530471
Variance2914001.093
2020-08-06T01:40:20.293296image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0566266.5%
 
17278.5%
 
22723.2%
 
31011.2%
 
4710.8%
 
5580.7%
 
6440.5%
 
7330.4%
 
8320.4%
 
10260.3%
 
Other values (404)148817.5%
 
ValueCountFrequency (%) 
0566266.5%
 
17278.5%
 
22723.2%
 
31011.2%
 
4710.8%
 
ValueCountFrequency (%) 
349264< 0.1%
 
311252< 0.1%
 
298683< 0.1%
 
282392< 0.1%
 
273203< 0.1%
 

video
Boolean

Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size66.5 KiB
0
8397
1
 
117
ValueCountFrequency (%) 
0839798.6%
 
11171.4%
 

Cases_x
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count107
Unique (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean750179.7269203664
Minimum12
Maximum2000702
Zeros0
Zeros (%)0.0%
Memory size66.5 KiB
2020-08-06T01:40:20.487286image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile1561
Q1102276
median669272
Q31288587
95-th percentile1837371
Maximum2000702
Range2000690
Interquartile range (IQR)1186311

Descriptive statistics

Standard deviation637881.8103
Coefficient of variation (CV)0.8503053167
Kurtosis-1.227022928
Mean750179.7269
Median Absolute Deviation (MAD)585181
Skewness0.3625637286
Sum6387030195
Variance4.068932039e+11
2020-08-06T01:40:20.652418image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
258252042.4%
 
7019961441.7%
 
1412051431.7%
 
337611361.6%
 
89171351.6%
 
6379741341.6%
 
9079081311.5%
 
15611281.5%
 
840911281.5%
 
438501281.5%
 
Other values (97)710383.4%
 
ValueCountFrequency (%) 
121< 0.1%
 
1390.1%
 
15170.2%
 
1670.1%
 
2460.1%
 
ValueCountFrequency (%) 
2000702370.4%
 
1979908490.6%
 
1961781490.6%
 
1944367410.5%
 
1926636330.4%
 

Deaths_x
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count103
Unique (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42540.72363166549
Minimum0
Maximum113631
Zeros34
Zeros (%)0.4%
Memory size66.5 KiB
2020-08-06T01:40:20.837539image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile43
Q12300
median35442
Q378497
95-th percentile107169
Maximum113631
Range113631
Interquartile range (IQR)76197

Descriptive statistics

Standard deviation39020.81055
Coefficient of variation (CV)0.9172577996
Kurtosis-1.348555261
Mean42540.72363
Median Absolute Deviation (MAD)34644
Skewness0.3805612915
Sum362191721
Variance1522623656
2020-08-06T01:40:20.989545image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
4562042.4%
 
380561441.7%
 
35611431.7%
 
6011361.6%
 
1881351.6%
 
333291341.6%
 
528671311.5%
 
7841281.5%
 
17461281.5%
 
431281.5%
 
Other values (93)710383.4%
 
ValueCountFrequency (%) 
0340.4%
 
1150.2%
 
6230.3%
 
750.1%
 
11130.2%
 
ValueCountFrequency (%) 
113631370.4%
 
112714490.6%
 
111774490.6%
 
111269410.5%
 
110818330.4%
 

word_count
Real number (ℝ≥0)

Distinct count62
Unique (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.55837444209537
Minimum4
Maximum84
Zeros0
Zeros (%)0.0%
Memory size66.5 KiB
2020-08-06T01:40:21.160947image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile16
Q135
median42
Q347
95-th percentile52
Maximum84
Range80
Interquartile range (IQR)12

Descriptive statistics

Standard deviation10.50829349
Coefficient of variation (CV)0.2656401745
Kurtosis0.4329630092
Mean39.55837444
Median Absolute Deviation (MAD)6
Skewness-0.9638101506
Sum336800
Variance110.4242321
2020-08-06T01:40:21.337555image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
424945.8%
 
434715.5%
 
454605.4%
 
464335.1%
 
474044.7%
 
444044.7%
 
483944.6%
 
413624.3%
 
493263.8%
 
393183.7%
 
Other values (52)444852.2%
 
ValueCountFrequency (%) 
42< 0.1%
 
72< 0.1%
 
82< 0.1%
 
9140.2%
 
10120.1%
 
ValueCountFrequency (%) 
841< 0.1%
 
781< 0.1%
 
671< 0.1%
 
661< 0.1%
 
641< 0.1%
 

avg_word_length
Real number (ℝ≥0)

Distinct count2438
Unique (%)28.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.543809359955163
Minimum3.25757575757575
Maximum31.75
Zeros0
Zeros (%)0.0%
Memory size66.5 KiB
2020-08-06T01:40:21.773011image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum3.257575758
5-th percentile4.186046512
Q14.6875
median5.179487179
Q36.0625
95-th percentile7.925640244
Maximum31.75
Range28.49242424
Interquartile range (IQR)1.375

Descriptive statistics

Standard deviation1.336497416
Coefficient of variation (CV)0.2410792525
Kurtosis40.64776619
Mean5.54380936
Median Absolute Deviation (MAD)0.6189993746
Skewness3.54408339
Sum47199.99289
Variance1.786225344
2020-08-06T01:40:21.941195image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
4.9761904761051.2%
 
5780.9%
 
6700.8%
 
4.75450.5%
 
7440.5%
 
4.5430.5%
 
4.893617021360.4%
 
4.6360.4%
 
4.490196078340.4%
 
4.770833333320.4%
 
Other values (2428)799193.9%
 
ValueCountFrequency (%) 
3.2575757581< 0.1%
 
3.4193548391< 0.1%
 
3.5272727271< 0.1%
 
3.5423728812< 0.1%
 
3.551< 0.1%
 
ValueCountFrequency (%) 
31.752< 0.1%
 
191< 0.1%
 
16.307692311< 0.1%
 
15.777777782< 0.1%
 
14.629629631< 0.1%
 

stopwords_count
Real number (ℝ≥0)

ZEROS

Distinct count35
Unique (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.521963824289406
Minimum0
Maximum34
Zeros90
Zeros (%)1.1%
Memory size66.5 KiB
2020-08-06T01:40:22.108656image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q111
median15
Q319
95-th percentile23
Maximum34
Range34
Interquartile range (IQR)8

Descriptive statistics

Standard deviation5.938369332
Coefficient of variation (CV)0.4089232974
Kurtosis-0.2697309713
Mean14.52196382
Median Absolute Deviation (MAD)4
Skewness-0.3005748253
Sum123640
Variance35.26423033
2020-08-06T01:40:22.298709image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
136948.2%
 
175896.9%
 
155886.9%
 
165686.7%
 
145356.3%
 
185126.0%
 
195045.9%
 
204825.7%
 
124435.2%
 
113854.5%
 
Other values (25)321437.7%
 
ValueCountFrequency (%) 
0901.1%
 
1490.6%
 
21191.4%
 
31912.2%
 
42012.4%
 
ValueCountFrequency (%) 
341< 0.1%
 
332< 0.1%
 
322< 0.1%
 
313< 0.1%
 
3070.1%
 

char_count
Real number (ℝ≥0)

Distinct count376
Unique (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean252.33192389006342
Minimum51
Maximum585
Zeros0
Zeros (%)0.0%
Memory size66.5 KiB
2020-08-06T01:40:22.486466image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum51
5-th percentile115
Q1226
median268
Q3280
95-th percentile342
Maximum585
Range534
Interquartile range (IQR)54

Descriptive statistics

Standard deviation64.70749952
Coefficient of variation (CV)0.2564380223
Kurtosis0.9104624038
Mean252.3319239
Median Absolute Deviation (MAD)25
Skewness-0.5212346096
Sum2148354
Variance4187.060494
2020-08-06T01:40:22.654020image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
2804014.7%
 
2793814.5%
 
2782843.3%
 
2772422.8%
 
2752022.4%
 
2762002.3%
 
2511641.9%
 
2741381.6%
 
2721301.5%
 
2711201.4%
 
Other values (366)625273.4%
 
ValueCountFrequency (%) 
511< 0.1%
 
591< 0.1%
 
662< 0.1%
 
683< 0.1%
 
723< 0.1%
 
ValueCountFrequency (%) 
5851< 0.1%
 
5841< 0.1%
 
5781< 0.1%
 
5191< 0.1%
 
5111< 0.1%
 

Sentiment
Real number (ℝ)

ZEROS

Distinct count1233
Unique (%)14.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0319829873673658
Minimum-1.0
Maximum1.0
Zeros1990
Zeros (%)23.4%
Memory size66.5 KiB
2020-08-06T01:40:22.829811image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-0.4
Q1-0.07222222222
median0
Q30.1666666667
95-th percentile0.45
Maximum1
Range2
Interquartile range (IQR)0.2388888889

Descriptive statistics

Standard deviation0.2572901493
Coefficient of variation (CV)8.044594031
Kurtosis1.954804969
Mean0.03198298737
Median Absolute Deviation (MAD)0.1258225108
Skewness-0.2200270062
Sum272.3031544
Variance0.06619822091
2020-08-06T01:40:23.003141image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0199023.4%
 
0.42753.2%
 
0.21942.3%
 
0.11752.1%
 
0.251722.0%
 
0.51561.8%
 
-0.21411.7%
 
-0.11311.5%
 
0.16666666671221.4%
 
-0.31161.4%
 
Other values (1223)504259.2%
 
ValueCountFrequency (%) 
-1330.4%
 
-0.980.1%
 
-0.851< 0.1%
 
-0.8310.4%
 
-0.8110.1%
 
ValueCountFrequency (%) 
1190.2%
 
0.92< 0.1%
 
0.854< 0.1%
 
0.8320.4%
 
0.84< 0.1%
 

Target
Real number (ℝ)

ZEROS

Distinct count13
Unique (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.49318769086211
Minimum-4.0
Maximum14.0
Zeros107
Zeros (%)1.3%
Memory size66.5 KiB
2020-08-06T01:40:23.182515image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum-4
5-th percentile3
Q14
median6
Q39
95-th percentile11
Maximum14
Range18
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.72697703
Coefficient of variation (CV)0.4199750815
Kurtosis0.1880294348
Mean6.493187691
Median Absolute Deviation (MAD)2
Skewness0.5614240962
Sum55283
Variance7.436403722
2020-08-06T01:40:23.354772image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
6232227.3%
 
4170020.0%
 
10119314.0%
 
57008.2%
 
96567.7%
 
75726.7%
 
34815.6%
 
142512.9%
 
82172.5%
 
112122.5%
 
Other values (3)2102.5%
 
ValueCountFrequency (%) 
-43< 0.1%
 
01071.3%
 
21001.2%
 
34815.6%
 
4170020.0%
 
ValueCountFrequency (%) 
142512.9%
 
112122.5%
 
10119314.0%
 
96567.7%
 
82172.5%
 

Interactions

2020-08-06T01:39:46.167959image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T01:39:46.344877image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T01:39:46.524482image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T01:39:46.695287image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-06T01:39:46.857706image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
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Correlations

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Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-08-06T01:40:23.920914image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-08-06T01:40:24.233957image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-08-06T01:40:24.543090image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

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2020-08-06T01:40:18.142305image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Sample

First rows

Unnamed: 0datelikes_countreplies_countretweets_countvideoCases_xDeaths_xword_countavg_word_lengthstopwords_countchar_countSentimentTarget
0020200211437446421890120415.536585162690.0000007.0
11202002130000130387.894737153380.3750007.0
22202002130000130378.500000153610.3750007.0
33202002130000130398.550000153820.0486117.0
44202002150000130348.457143103300.2166676.0
55202002165000130415.950000182780.0000007.0
66202002165000130415.950000182780.0000007.0
77202002172100130397.128205113170.1787887.0
8820200219885470130136.0833333860.000000-4.0
9920200219895470130136.0833333860.000000-4.0

Last rows

Unnamed: 0datelikes_countreplies_countretweets_countvideoCases_xDeaths_xword_countavg_word_lengthstopwords_countchar_countSentimentTarget
850485042020061010002000702113631323.718750151500.0000008.0
850585052020061001002000702113631355.485714142260.1000008.0
850685062020061010002000702113631564.01785722280-0.1603178.0
850785072020061000002000702113631574.18867924278-0.0700898.0
850885082020061010002000702113631408.536585103920.0000008.0
850985092020061000002000702113631513.882353212480.4333338.0
851085102020061001002000702113631554.12963022277-0.3547628.0
851185112020061000002000702113631503.97916717240-0.2666678.0
8512851220200610159284002000702113631365.722222132420.2500008.0
851385132020061000002000702113631474.782609152660.0500008.0